hadoop

Distributed XGBoost: Hadoop Version

• The script in this fold shows an example of how to run distributed xgboost on hadoop platform.
• It relies on Rabit Library (Reliable Allreduce and Broadcast Interface) and Hadoop Streaming. Rabit provides an interface to aggregate gradient values and split statistics, that allow xgboost to run reliably on hadoop. You do not need to care how to update model in each iteration, just use the script rabit_hadoop.py. For those who want to know how it exactly works, plz refer to the main page of Rabit.
• Quick start: run bash run_mushroom.sh <n_hadoop_workers> <n_thread_per_worker> <path_in_HDFS>

• This is the hadoop version of binary classification example in the demo folder.
• More info of the usage of xgboost can be refered to wiki page

Before you run the script

• Make sure you have set up the hadoop environment.
• If you want to only use single machine multi-threading, try single machine examples in the demo folder.
• Build: run bash build.sh in the root folder, it will automatically download rabit and build xgboost.
• Check whether the environment variable $HADOOP_HOME exists (e.g. run echo $HADOOP_HOME). If not, please set up hadoop-streaming.jar path in rabit_hadoop.py.

How to Use

• Input data format: LIBSVM format. The example here uses generated data in demo/data folder.
• Put the training data in HDFS (hadoop distributed file system).
• Use rabit rabit_hadoop.py to submit training task to hadoop, and save the final model file.
• Get the final model file from HDFS, and locally do prediction as well as visualization of model.

Single machine vs Hadoop version

If you have used xgboost (single machine version) before, this section will show you how to run xgboost on hadoop with a slight modification on conf file.

• Hadoop version needs to set up how many slave nodes/machines/workers you would like to use at first.
• IO: instead of reading and writing file locally, hadoop version use "stdin" to read training file and use "stdout" to store the final model file. Therefore, you should change the parameters "data" and "model_out" in conf file to data=stdin and model_out=stdout.
• File cache: rabit_hadoop.py also provide several ways to cache necesary files, including binary file (xgboost), conf file, small size of dataset which used for eveluation during the training process, and so on.
  • Any file used in config file, excluding stdin, should be cached in the script. rabit_hadoop.py will automatically cache files in the command line. For example, rabit_hadoop.py -n 3 -i $hdfsPath/agaricus.txt.train -o $hdfsPath/mushroom.final.model $localPath/xgboost mushroom.hadoop.conf will cache "xgboost" and "mushroom.hadoop.conf".
  • You could also use "-f" to manually cache one or more files, like -f file1 -f file2 or -f file1#file2 (use "#" to spilt file names).
  • The local path of cached files in command is "./".
  • Since the cached files will be packaged and delivered to hadoop slave nodes, the cached file should not be large. For instance, trying to cache files of GB size may reduce the performance.
• Hadoop version also support evaluting each training round. You just need to modify parameters "eval_train".
• More details of submission can be referred to the usage of rabit_hadoop.py.
• The model saved by hadoop version is compatible with single machine version.

Notes

• The code has been tested on MapReduce 1 (MRv1) and YARN.
  • We recommend to run it on MapReduce 2 (MRv2, YARN) so that multi-threading can be enabled.
• The code is optimized with multi-threading, so you will want to run one xgboost per node/worker for best performance.
  • You will want to set <n_thread_per_worker> to be number of cores you have on each machine.
  • You will need YARN to set specify number of cores of each worker